The proliferation of the “Internet of Things” (IoT) has enabled users and applications to communicate, control, and automate connected devices. Sensors and actuators may be used to improve quality of experience, peace of mind, and the cost of operations in smart home, office, building, or city environments. Since each IoT device may generate a large amount of data about users, devices, and environments, it may be challenging to analyze the data from multiple IoT sensors.
Various communication protocols and data model standards are under development to support the integration and management of IoT devices by multiple applications and services (e.g., smart energy management, fitness applications, and healthcare services). In addition, many sensor data fusion, visualization, event detection, and integrated management solutions have been proposed or are under investigation for improving user experiences, system efficiency, and reliability. Machine learning methods may be used for pattern acquisition. The patterns learned may be used to improve prediction accuracy.
As more IoT devices and applications are integrated, the increased interactions between user behavior, environment conditions, and applications may increase the complexities of IoT management operations and user interfaces. It may be a challenge to reliably provide useful control actions to actuators in real-time. It may also be difficult to track what actions are useful to end users for improving operations.